The most important facts in brief
Data quality management (DQM) is the key to systematically ensuring the quality of company data and translating it into measurable value for management, compliance and strategic decisions. This is not just about correct master data – it is about an end-to-end management model that controls, evaluates and improves data from the source to reporting. Qlik enables the automatic measurement of completeness, timeliness and accuracy. Corporate Planner translates this quality information into economic effects, for example by calculating the “cost-of-poor-data” and prioritizing improvement measures. This means that DQM is no longer a one-off project, but a regular cycle – with concrete KPIs, responsibilities and a visible ROI.
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Beratungstermin vereinbarenWhat is Data Quality Management and why is it business-critical?
Data quality management (DQM) describes the strategic and operational framework that companies use to systematically ensure the quality of their data. It is not just about technical aspects, but about an organization-wide concept that encompasses processes, responsibilities, tools and standards. The aim is to provide valid, consistent and usable data – across all systems, from operations to corporate management.
In modern IT landscapes in which SAP, data governance frameworks and business intelligence platforms such as Qlik are used, business success increasingly depends on the quality of the data used. Poor data quality not only causes costs, but also jeopardizes analyses, forecasts and strategic decisions. Studies show that More than 40 percent of all business decisions are based on incorrect or incomplete information.
Data quality management is therefore not an isolated IT project, but a crucial building block for resilient business processes, regulatory requirements and data-driven corporate management. Successful DQM starts with data collection, extends to data maintenance, automation and validation, right through to impact analysis and continuous improvement. Integrated into master data governance and aligned with data strategy and organizational structure, DQM creates real, measurable added value.
The five dimensions of data quality at a glance
Data quality is not an abstract concept, but can be measured and improved according to specific criteria. The common dimensions are: Completeness, accuracy, consistency, timeliness and relevance. They form the basis for evaluating, prioritizing and controlling data quality measures.
Completeness describes whether all the necessary data is available – for example, complete master data records in customer management.
Accuracy refers to the comparison with reality: Are addresses, prices or times actually correct?
Consistency means that data is consistent across different systems – for example, if article numbers and product specifications are identical in CRM and ERP.
Up-to-dateness checks whether the data reflects the latest status – a particularly important factor in reporting and decision-making.
Relevance finally assesses whether the available information is actually necessary and useful for the task in question.
These dimensions form the basis for standardized data management and are a prerequisite for integration into operational processes. Tools such as Qlik enable the automated collection and visualization of data quality values. Dashboards can be used to quickly identify where there is a need for action – be it in the provision of data for ESG reports, product information in e-commerce or customer analyses in sales.
Typische KPIs für Datenqualität
Anteil vollständig gepflegter Datensätze je Kategorie.
Prozentualer Anteil korrekt validierter Informationen.
Abweichungsquote zwischen Systemen oder Quellen.
Daten mit akzeptablem Änderungsintervall (z. B.
Nutzungsquote der bereitgestellten Daten im operativen Prozess.
The control loop in DQM – from the source system to controlling
Effective data quality management follows a structured cycle that monitors and optimizes data along its entire value chain. The focus is not only on identifying problems, but above all on the targeted derivation, prioritization and management of measures – right through to financial evaluation.
The process begins with the identification of critical data sources: operational ERP systems, CRM, product databases or external supplier information. From there, the data is collected according to defined quality standards and systematically checked for completeness, integrity and accuracy. Deviations – such as outdated data records, incorrect entries or missing fields – are automatically flagged.
Qlik is used here as an analysis tool: it not only measures the deviations, but also visualizes them according to business function, data source and process context. Particularly important: the data is not viewed in isolation, but is linked to its lineage – its origin and use.
Corporate Planner takes over control in the second step: data quality problems are translated into monetary effects – the so-called “cost-of-poor data”. From this, specific packages of measures with an expected € impact can be derived, prioritized and budgeted. This creates a PDCA cycle (Plan – Do – Check – Act) that turns quality assurance into a continuously controlled management task.
Making data quality measurable – KPIs, dashboards and economic benefits
Data quality management only unfolds its full value when it can be measured objectively. This requires clear quality indicators, regular evaluations and a transparent link to the economic impact. Companies that create this transparency can make much more informed data-driven decisions.
Typical quality metrics include the error rate in customer master data, the up-to-dateness of supplier data or the proportion of missing mandatory fields in product information. Such indicators can be automatically recorded and visualized in dashboards – broken down by data source, process or department. This creates a reliable picture of the data situation in day-to-day business.
The whole thing becomes even more effective when quality is linked to economic effects. For example, if inadequate data leads to returns, contract risks or wrong decisions. By systematically recording and evaluating these effects, priorities can be set – not only technically, but above all financially.
The introduction of such control mechanisms marks a turning point: selective clean-up actions become a continuously controlled improvement process – embedded in clear responsibilities and strategic goals.
Typische Kosten schlechter Daten
Unvollständige Kundendaten führen zu falschen Angeboten und sinkenden Abschlussraten.
Fehlerhafte Stammdaten verursachen Lieferverzögerungen und teure Retouren.
Veraltete oder inkonsistente Daten erschweren eine rechtssichere Berichterstattung.
Ungenaue Materialdaten können Prozesse blockieren und Kosten massiv in die Höhe treiben.
From an ongoing problem to a routine task – DQM as a sustainable management process
Many companies start data quality management as a project – often due to an acute need or regulatory pressure. However, DQM can only deliver strategic benefits if it is established as a permanent process. The aim is to anchor data quality as a natural part of daily work – in systems, responsibilities, KPIs and decisions.
This requires three prerequisites: firstly, a clear organizational framework that defines roles such as data owner, data steward and central governance functions. Secondly, a methodical approach based on standards, measurement criteria and continuous improvement. And thirdly, technological solutions that automatically monitor data volumes, detect anomalies at an early stage and prepare the results in an understandable way.
Companies that succeed in this triad not only gain in efficiency and security, but also in their ability to innovate. This is because valid data is not just an obligation – it is a prerequisite for excellent products, reliable services, sound analyses and robust decisions.
In this way, DQM is transformed from an isolated reaction to data problems into an active component of a professional data strategy – integrated into business processes, product development, customer service and controlling. The effort is reduced, the added value increases.
Frequently asked questions about Data Quality Management
What is Data Quality Management (DQM)?
DQM describes all strategic, organizational and technical measures to ensure reliable databases. The aim is to optimize the database for company-wide processes, decisions and analyses through targeted data management, quality controls and clear responsibilities.
Why is data quality crucial to a company’s success?
High data quality enables precise data analysis, reduces operational risks and supports the implementation of strategic goals. Without systematic assurance, processes are prone to errors and decisions are uncertain – with a direct impact on sales, costs and customer satisfaction.
What role do technologies and tools play in implementation?
Modern technologies help to automatically identify data quality problems, implement rules and monitor activities. However, they do not replace the need for structured implementation – with defined processes, training and embedded governance.
How can the concrete benefits of DQM be demonstrated?
One effective way is to link quality indicators with business objectives. Example: The analysis of “cost-of-poor-data” shows the financial consequences of incorrect data – for example due to errors, incorrect reports or compliance risks. Prioritized measures and concrete savings potential can be derived from this.
